Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability

Fee, M. S., Mitra, P. P., Kleinfeld, D. (November 1996) Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability. Journal of Neuroscience Methods, 69 (2). pp. 175-188. ISSN 0165-0270

DOI: 10.1016/S0165-0270(96)00050-7


Neuronal noise sources and systematic variability in the shape of a spike limit the ability to sort multiple unit waveforms recorded from nervous tissue into their single neuron constituents. Here we present a procedure to efficiently sort spikes in the presence of noise that is anisotropic, i.e., dominated by particular frequencies, and whose amplitude distribution may be non-Gaussian, such as occurs when spike waveforms are a function of interspike interval. Our algorithm uses a hierarchical clustering scheme. First, multiple unit records are sorted into an overly large number of clusters by recursive bisection. Second, these clusters are progressively aggregated into a minimal set of putative single units based on both similarities of spike shape as well as the statistics of spike arrival times, such as imposed by the refractory period. We apply the algorithm to waveforms recorded with chronically implanted micro-wire stereotrodes from neocortex of behaving rat. Natural extensions of the algorithm may be used to cluster spike waveforms from records with many input channels, such as those obtained with tetrodes and multiple site optical techniques.

Item Type: Paper
Additional Information:
Uncontrolled Keywords: Extracellular recording Multi-unit spike train Waveform variability Stereotrode Neocortex
Subjects: physics > neurophysics
organs, tissues, organelles, cell types and functions > tissues types and functions > signal transduction
CSHL Authors:
Communities: CSHL labs > Mitra lab
Depositing User: CSHL Librarian
Date: November 1996
Date Deposited: 04 Apr 2012 18:20
Last Modified: 10 Feb 2017 17:23
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